class_docstr: Web sessions information from an events company based in South Africa.
  
  The goal is to predict the number of web sessions in 4 different regions in South Africa.
  
  The data consists of 15 minute interval traffic values between '2023-06-16 00:00:00' and
  '2023-09-15 23:45:00' for each region. Two types of sessions are captured `sessionsA` and
  `sessionsB`. The `isMissing` flag is equal to 1 if any of the servers failed to capture
  sessions, otherwise if all servers functioned properly this flag is equal to 0.
  
  Things to consider:
  
  * region `R5` captures sessions in backup mode. Strictly speaking, `R5` is not necessary to predict.
  * Can `sessionsA` and `sessionsB` events be predicted accurately for each region over the next day (next 96 intervals)?
  * What is the best way to deal with the missing values?
  * How can model selection be used (a multi-model approach)?
  * Can dependence (correlation) between regions be utilised for more accurate predictions?
  * Can both `sessionA` and `sessionB` be predicted simultaneously with one model?
  
  This dataset is well suited for time series forecasting models, as well as anomaly detection
  methods. Ideally, the goal is to build a time series forecasting model that is robust to the
  anomalous events and generalise well on normal operating conditions.
code: def _iter(self):
      return stream.iter_csv(
          self.path,
          dialect=PipeCSVDialect,
          target=["sessionsA", "sessionsB"],
          converters={
              "region": str,
              "isMissing": lambda x: x == "1.0",
              "sessionsA": lambda x: float(x)
              if x and x != "0.0"
              else None,
              "sessionsB": lambda x: float(x)
              if x and x != "0.0"
              else None,
          },
          parse_dates={"dateTime": "%Y-%m-%d %H:%M:%S"},
      )
  
file_path: river/river/datasets/web_traffic.py
imports: from __future__ import annotations
  import csv
  from river import stream
  from .None import base
label_desc: Use image node_id 2 for calling the WebTraffic obj's underlying member method code with example usage: obj._iter() and returns: stream